All examples for Hypothesis Prioritization Matrix

B2B SaaS Sign-ups Drop (Incomplete Information)

Edge case with vague input: 'dropped significantly' without specific numbers. Demonstrates how the skill handles incomplete information by stating explicit assumptions, providing clarifying questions, and still delivering a complete analysis.

Input

What you provide to the skill

Sign-ups dropped significantly. It’s a B2B SaaS product. Need to figure out why.

Output

What the skill generates for you

SCENARIO: B2B SaaS sign-ups dropped significantly

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ASSUMPTIONS & CLARIFYING QUESTIONS
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ASSUMPTIONS (proceeding with these):
• Magnitude: ~30-50% drop (assuming “significant” means substantial)
• Timeframe: Past 1-2 weeks (typical detection window)
• Metric: New account sign-ups (trial or paid)
• Context: Self-serve B2B SaaS with web-based signup flow

CLARIFYING QUESTIONS (for interview context):
• What’s the exact percentage drop?
• When exactly did this start?
• Is this trial sign-ups, paid sign-ups, or both?
• What’s the typical acquisition channel mix?
• Has there been any recent product/marketing changes?

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GENERATED HYPOTHESES (18 total)
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CATEGORY: TECHNICAL
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  1. Sign-up form broken or not submitting
    Impact: 5/5 | Likelihood: 4/5 | Ease: 5/5 | PRIORITY: 100
    Rationale: Complete blocker to conversions; test form immediately

  2. Email verification system failing
    Impact: 4/5 | Likelihood: 3/5 | Ease: 5/5 | PRIORITY: 60
    Rationale: Users can’t activate accounts; check email logs

  3. Payment gateway integration broken (for paid plans)
    Impact: 5/5 | Likelihood: 3/5 | Ease: 5/5 | PRIORITY: 75
    Rationale: Blocks paid conversions; check Stripe/payment logs

  4. Page load time degraded significantly
    Impact: 4/5 | Likelihood: 3/5 | Ease: 5/5 | PRIORITY: 60
    Rationale: Slow pages kill conversions; check performance monitoring

CATEGORY: PRODUCT
─────────────────────────────────────────────────────────

  1. Sign-up flow redesigned with more friction
    Impact: 5/5 | Likelihood: 4/5 | Ease: 5/5 | PRIORITY: 100
    Rationale: Adding fields or steps reduces completion; check changelog

  2. Pricing page changed (price increase or visibility)
    Impact: 5/5 | Likelihood: 3/5 | Ease: 5/5 | PRIORITY: 75
    Rationale: Price shock kills B2B trials; check pricing history

  3. Free trial removed or shortened
    Impact: 5/5 | Likelihood: 2/5 | Ease: 5/5 | PRIORITY: 50
    Rationale: Major friction change; review product updates

  4. CTA buttons moved or removed from key pages
    Impact: 4/5 | Likelihood: 3/5 | Ease: 4/5 | PRIORITY: 48
    Rationale: Navigation impacts discoverability; A/B test data

CATEGORY: MARKETING/EXTERNAL
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  1. Paid advertising campaign paused or budget cut
    Impact: 5/5 | Likelihood: 5/5 | Ease: 5/5 | PRIORITY: 125
    Rationale: Direct traffic driver; check ad spend instantly

  2. SEO ranking dropped for key acquisition terms
    Impact: 4/5 | Likelihood: 3/5 | Ease: 4/5 | PRIORITY: 48
    Rationale: Organic traffic decline; check Search Console

  3. Major competitor launched aggressive promotion
    Impact: 3/5 | Likelihood: 3/5 | Ease: 3/5 | PRIORITY: 27
    Rationale: Market shift; competitor intelligence check

  4. Email marketing campaign stopped or bouncing
    Impact: 4/5 | Likelihood: 3/5 | Ease: 5/5 | PRIORITY: 60
    Rationale: Re-engagement driver; check email delivery rates

CATEGORY: ACQUISITION SEGMENTATION
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  1. Specific traffic channel dropped (e.g., Google Ads)
    Impact: 5/5 | Likelihood: 4/5 | Ease: 5/5 | PRIORITY: 100
    Rationale: If one channel = 80% of traffic, narrows scope dramatically

  2. Geographic concentration (one region/country blocked)
    Impact: 4/5 | Likelihood: 2/5 | Ease: 5/5 | PRIORITY: 40
    Rationale: Regional issue; segment by country

  3. Landing page-specific issue (e.g., one campaign page broken)
    Impact: 4/5 | Likelihood: 3/5 | Ease: 5/5 | PRIORITY: 60
    Rationale: Campaign-specific drop; check by landing page

CATEGORY: DATA QUALITY
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  1. Analytics tracking broke (GA4/Segment)
    Impact: 5/5 | Likelihood: 3/5 | Ease: 4/5 | PRIORITY: 60
    Rationale: False alarm; validate raw database signups vs analytics

  2. Sign-up definition changed (e.g., excluding trials)
    Impact: 5/5 | Likelihood: 2/5 | Ease: 5/5 | PRIORITY: 50
    Rationale: Metric change not real; check dashboard configuration

  3. Bot traffic previously inflated numbers
    Impact: 3/5 | Likelihood: 2/5 | Ease: 3/5 | PRIORITY: 18
    Rationale: Bot detection improved; check for non-human traffic patterns

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PRIORITIZATION MATRIX (Impact × Likelihood)
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                HIGH IMPACT

                    ↑

     ─────────────────────────────────

     │                    │           │

HIGH │   #9 (Ad spend)    │  #3, #6   │

LIKELI-│ #1 (Form broken) │ (Medium) │
HOOD │ #5 (Flow change) │ │
│ #13 (Channel) │ │
│ CHECK FIRST │ │
├────────────────────┼───────────┤
│ #2, #4, #12, #15 │ #7, #11 │
│ #16 (Tracking) │ #14, #17 │
LOW │ QUICK WINS │ #18 │
│ │ (Lower │
│ │ Priority) │
─────────────────────────────────
→
HIGH LIKELIHOOD

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INVESTIGATION SEQUENCE
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⚡ WAVE 1 (5-7 minutes total)
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→ #9: Ad spend check
Action: Review marketing spend by channel for past 2 weeks
Time: 1 minute
Info gain: If spend dropped 50%, explains entire drop instantly

→ #13: Traffic channel segmentation
Action: Segment sign-ups by acquisition channel (organic, paid, direct, referral)
Time: 1 minute
Info gain: If 80% drop in one channel, narrows scope dramatically

→ #1: Sign-up form functionality
Action: Test sign-up flow manually + check form submission rate
Time: 2 minutes
Info gain: Clear yes/no on complete blocker

→ #5: Recent product changes
Action: Check deployment logs and product changelog for past 2 weeks
Time: 2 minutes
Info gain: Temporal correlation establishes causation candidate

→ #16: Data validation
Action: Compare database sign-up count vs analytics dashboard
Time: 1 minute
Info gain: Rules in/out false alarm (tracking issue)

⏱️ WAVE 2 (10-15 minutes)
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→ #3: Payment gateway - Check transaction success rate
→ #6: Pricing changes - Review pricing page history
→ #2: Email verification - Check email delivery logs
→ #15: Landing page performance - Segment by entry page
→ #4: Page performance - Check Core Web Vitals/load times
→ #10: SEO ranking - Check Google Search Console data

🔍 WAVE 3 (If needed)
─────────────────────────────────────────────────────────

→ #7, #8, #11, #12, #14, #17, #18 - Lower priority hypotheses

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DATA REQUESTS (Priority Order)
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Request 1: Marketing Spend Check
“Show me marketing spend by channel (Google Ads, LinkedIn, organic, etc.) for past 2 weeks vs prior 2 weeks”
Time: 1 minute | Rules in/out: Budget cut or campaign pause

Request 2: Acquisition Channel Breakdown
“Show me sign-ups by acquisition source/channel for past 2 weeks vs prior 2 weeks”
Time: 1 minute | Rules in/out: Channel-specific issue

Request 3: Sign-up Funnel Metrics
“Show me conversion rates at each step of sign-up flow: landing → form start → form submit → email verify → activated”
Time: 2 minutes | Rules in/out: Which step broke

Request 4: Recent Deployments
“List all production deployments in past 2 weeks with descriptions”
Time: 2 minutes | Rules in/out: Product change correlation

Request 5: Data Quality Validation
“Compare raw database INSERT count on users table vs analytics sign-up events”
Time: 1 minute | Rules in/out: Tracking failure (false alarm)

Request 6: Landing Page Performance
“Show me sign-ups by landing page URL for past 2 weeks”
Time: 1 minute | Rules in/out: Campaign-specific page issue

Request 7: Payment Success Rate (if applicable)
“What’s the payment transaction success rate past 2 weeks vs prior?”
Time: 1 minute | Rules in/out: Payment gateway failure

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INTERVIEW TALKING POINTS
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“For B2B SaaS sign-up drops, I’ve identified 18 hypotheses across 5 MECE
categories: Technical, Product, Marketing/External, Acquisition Segmentation,
and Data Quality.

I prioritize using Impact × Likelihood × Ease-to-check. For B2B SaaS
specifically, marketing spend changes are the highest-probability root cause,
followed by acquisition channel issues and product flow changes.

Wave 1 (5-7 minutes) focuses on maximum information gain:

• Marketing spend check (1min) - If ad budget was cut or paused, this
explains the drop immediately. B2B SaaS often depends heavily on paid channels.

• Channel segmentation (1min) - If Google Ads = 70% of sign-ups and that
channel dropped, I’ve isolated the problem in 60 seconds.

• Form functionality test (2min) - Complete blocker would be obvious, but
checking form submission rates catches subtle breakage.

• Recent deployments (2min) - Temporal correlation is fast to establish
and narrows causation candidates.

• Data validation (1min) - Confirms this isn’t a false alarm from broken
analytics tracking.

The sequencing maximizes information gain per minute invested. Rather than
exploring all 18 hypotheses, these 5 checks will likely identify or narrow
to root cause, allowing targeted deep-dive in Wave 2.”

View all examples for Hypothesis Prioritization Matrix

About This Skill

Generate structured hypothesis prioritization matrices for PM interview root cause analysis with MECE categorization, scoring, and investigation sequences.

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